Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks.

This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis - linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.

The complete chloroplast genome of Codonopsis lanceolata (Campanulaceae).

The complete chloroplast genome sequence of Codonopsis lanceolata was determined by next generation sequencing. The total length of chloroplast genome of C. lanceolata was 169,447 bp long, including a large single-copy (LSC) region of 85,253 bp, a small single-copy (SSC) region of 8060 bp, and a pair of identical inverted repeat regions (IRs) of 38,067 bp. A total of 110 genes was annotated, resulting in 79 protein-coding genes, 27 tRNA genes, and 4 rRNA genes. The phylogenetic analysis of C. lanceolata with related chloroplast genome sequences in this study provided the taxonomical relationship of C. lanceolata in the genus Campanula.